Study 1 (4a)

Descriptives and Chart

Prime Type Risk Response Proportion SD N
Beer High 0.564 0.181 112
Beer Low 0.390 0.168 112
Neutral High 0.482 0.180 112
Neutral Low 0.459 0.164 112
Water High 0.502 0.183 112
Water Low 0.454 0.168 112

Charts including condition

DV: Proportion of high risk responses - proportion of low risk responses

DV: Risk variable: number of high risk responses/(number of high risk + low risk responses)

Models

Simple Effects

Tests if the difference in propotions of high-risk responses is different from the proportions of low-risk for each prime category.

Beer

## 
## Call:
## lm(formula = beer.dif ~ 1, data = gamble.w)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -24.1696  -4.4196   0.8304   5.8304  17.8304 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   4.1696     0.7823    5.33 5.21e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.279 on 111 degrees of freedom

Water

## 
## Call:
## lm(formula = water.dif ~ 1, data = gamble.w)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -20.1518  -4.1518   0.8482   5.8482  17.8482 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)   1.1518     0.7861   1.465    0.146
## 
## Residual standard error: 8.319 on 111 degrees of freedom

Neutral

## 
## Call:
## lm(formula = neut.dif ~ 1, data = gamble.w)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -19.5357  -4.7857   0.4643   5.4643  23.4643 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)   0.5357     0.7654     0.7    0.485
## 
## Residual standard error: 8.101 on 111 degrees of freedom

Mixed Effects Models

Beer vs Water

This model tests the effects of a Beer vs a Water prime on risk response.
Fixed effects:
       Beer/Water contrast: Beer = +1/2, Water = -1/2, Neutral = 0
       Orthogonal contrast: Beer = +1/3, Water = +1/3, Neutral = -2/3
Random effects:
       Subject: Intercept, slope for each contrast
       Stimuli(pic2): Intercept

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: response ~ BvW + BWvN + (1 + BvW + BWvN | Subject) + (1 | pic2)
##    Data: gamble
## 
##      AIC      BIC   logLik deviance df.resid 
##  10174.6  10244.1  -5077.3  10154.6     7652 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3901 -0.9605  0.5990  0.8288  2.5831 
## 
## Random effects:
##  Groups  Name        Variance Std.Dev. Corr       
##  Subject (Intercept) 0.31142  0.5581              
##          BvW         0.64767  0.8048   -0.11      
##          BWvN        0.29586  0.5439   -0.05  0.32
##  pic2    (Intercept) 0.01092  0.1045              
## Number of obs: 7662, groups:  Subject, 112; pic2, 12
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)   
## (Intercept)  0.16979    0.06548   2.593  0.00951 **
## BvW          0.30029    0.12171   2.467  0.01361 * 
## BWvN         0.20470    0.09689   2.113  0.03462 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##      (Intr) BvW   
## BvW  -0.054       
## BWvN -0.021  0.110

This model adds the Alcohol Quantity-Frequency Product (AlcQF) and its interactions to the list of fixed effects and calculates an AlcQF slope for each stimuli picture.

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: response ~ BvW + BWvN + AlcQF * BvW + AlcQF * BWvN + (1 + BvW +  
##     BWvN | Subject) + (1 + AlcQF | pic2)
##    Data: gamble
## 
##      AIC      BIC   logLik deviance df.resid 
##   9638.4   9741.7  -4804.2   9608.4     7245 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.4242 -0.9552  0.5908  0.8302  2.6244 
## 
## Random effects:
##  Groups  Name        Variance  Std.Dev. Corr       
##  Subject (Intercept) 3.188e-01 0.564630            
##          BvW         6.405e-01 0.800337 -0.10      
##          BWvN        2.900e-01 0.538481 -0.01  0.43
##  pic2    (Intercept) 1.290e-02 0.113569            
##          AlcQF       1.541e-05 0.003926 -0.30      
## Number of obs: 7260, groups:  Subject, 106; pic2, 12
## 
## Fixed effects:
##              Estimate Std. Error z value Pr(>|z|)  
## (Intercept)  0.167531   0.068664   2.440   0.0147 *
## BvW          0.282476   0.127581   2.214   0.0268 *
## BWvN         0.224964   0.101843   2.209   0.0272 *
## AlcQF       -0.006184   0.005952  -1.039   0.2988  
## BvW:AlcQF    0.004975   0.009971   0.499   0.6178  
## BWvN:AlcQF   0.008492   0.007618   1.115   0.2649  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) BvW    BWvN   AlcQF  BW:AQF
## BvW        -0.045                            
## BWvN       -0.003  0.138                     
## AlcQF      -0.025 -0.001 -0.001              
## BvW:AlcQF  -0.001 -0.049 -0.001 -0.067       
## BWvN:AlcQF -0.002 -0.001 -0.058 -0.009  0.217

Beer vs Neutral

This model tests the effects of a Beer vs a Neutral prime on risk response.
Fixed effects:
       Beer/Neutral contrast: Beer = +1/2, Neutral = -1/2, Water = 0
       Orthogonal contrast: Beer = +1/3, Neutral = +1/3, Water = -2/3
Random effects:
       Subject: Intercept, slope for each contrast
       Stimuli(pic2): Intercept

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: response ~ BvN + BNvW + (1 + BvW + BWvN | Subject) + (1 | pic2)
##    Data: gamble
## 
##      AIC      BIC   logLik deviance df.resid 
##  10174.6  10244.1  -5077.3  10154.6     7652 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3900 -0.9605  0.5990  0.8288  2.5831 
## 
## Random effects:
##  Groups  Name        Variance Std.Dev. Corr       
##  Subject (Intercept) 0.31142  0.5580              
##          BvW         0.64766  0.8048   -0.11      
##          BWvN        0.29585  0.5439   -0.05  0.32
##  pic2    (Intercept) 0.01092  0.1045              
## Number of obs: 7662, groups:  Subject, 112; pic2, 12
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)   
## (Intercept)  0.16978    0.06548   2.593  0.00951 **
## BvN          0.35487    0.11996   2.958  0.00309 **
## BNvW         0.12286    0.09850   1.247  0.21230   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##      (Intr) BvN   
## BvN  -0.044       
## BNvW -0.039  0.128

This model adds the Alcohol Quantity-Frequency Product (AlcQF) and its interactions to the list of fixed effects and calculates an AlcQF slope for each stimuli picture.

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: response ~ BvN + BNvW + AlcQF * BvN + AlcQF * BNvW + (1 + BvN +  
##     BNvW | Subject) + (1 + AlcQF | pic2)
##    Data: gamble
## 
##      AIC      BIC   logLik deviance df.resid 
##   9638.4   9741.7  -4804.2   9608.4     7245 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.4242 -0.9552  0.5908  0.8302  2.6244 
## 
## Random effects:
##  Groups  Name        Variance  Std.Dev. Corr       
##  Subject (Intercept) 3.188e-01 0.564629            
##          BvN         6.359e-01 0.797464 -0.06      
##          BNvW        2.934e-01 0.541663 -0.10  0.44
##  pic2    (Intercept) 1.290e-02 0.113568            
##          AlcQF       1.541e-05 0.003925 -0.30      
## Number of obs: 7260, groups:  Subject, 106; pic2, 12
## 
## Fixed effects:
##               Estimate Std. Error z value Pr(>|z|)   
## (Intercept)  0.1675359  0.0686622   2.440  0.01469 * 
## BvN          0.3662160  0.1274089   2.874  0.00405 **
## BNvW         0.0993781  0.1019915   0.974  0.32987   
## AlcQF       -0.0061835  0.0059515  -1.039  0.29881   
## BvN:AlcQF    0.0109790  0.0099699   1.101  0.27081   
## BNvW:AlcQF  -0.0005151  0.0076182  -0.068  0.94609   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) BvN    BNvW   AlcQF  BN:AQF
## BvN        -0.025                            
## BNvW       -0.041  0.140                     
## AlcQF      -0.025 -0.002  0.000              
## BvN:AlcQF  -0.002 -0.048 -0.002 -0.040       
## BNvW:AlcQF  0.000 -0.002 -0.059 -0.061  0.218

Water vs Neutral

This model tests the effects of a Water vs a Neutral prime on risk response.
Fixed effects:
       Water/Neutral contrast: Water = +1/2, Neutral = -1/2, Beer = 0
       Orthogonal contrast: Water = +1/3, Neutral = +1/3, Beer = -2/3
Random effects:
       Subject: Intercept, slope for each contrast
       Stimuli(pic2): Intercept

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: response ~ WvN + WNvB + (1 + BvW + BWvN | Subject) + (1 | pic2)
##    Data: gamble
## 
##      AIC      BIC   logLik deviance df.resid 
##  10174.6  10244.1  -5077.3  10154.6     7652 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3900 -0.9605  0.5990  0.8288  2.5831 
## 
## Random effects:
##  Groups  Name        Variance Std.Dev. Corr       
##  Subject (Intercept) 0.31142  0.5581              
##          BvW         0.64766  0.8048   -0.11      
##          BWvN        0.29585  0.5439   -0.05  0.32
##  pic2    (Intercept) 0.01092  0.1045              
## Number of obs: 7662, groups:  Subject, 112; pic2, 12
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)   
## (Intercept)  0.16978    0.06548   2.593  0.00951 **
## WvN          0.05458    0.10858   0.503  0.61523   
## WNvB        -0.32758    0.10796  -3.034  0.00241 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##      (Intr) WvN  
## WvN  0.011       
## WNvB 0.055  0.018

This model adds the Alcohol Quantity-Frequency Product (AlcQF) and its interactions to the list of fixed effects and calculates an AlcQF slope for each stimuli picture.

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: response ~ WvN + WNvB + AlcQF * WvN + AlcQF * WNvB + (1 + WvN +  
##     WNvB | Subject) + (1 + AlcQF | pic2)
##    Data: gamble
## 
##      AIC      BIC   logLik deviance df.resid 
##   9638.4   9741.7  -4804.2   9608.4     7245 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.4242 -0.9552  0.5908  0.8302  2.6244 
## 
## Random effects:
##  Groups  Name        Variance  Std.Dev. Corr      
##  Subject (Intercept) 3.188e-01 0.564626           
##          WvN         2.643e-01 0.514054 0.07      
##          WNvB        5.722e-01 0.756439 0.08  0.01
##  pic2    (Intercept) 1.290e-02 0.113568           
##          AlcQF       1.542e-05 0.003926 -0.30     
## Number of obs: 7260, groups:  Subject, 106; pic2, 12
## 
## Fixed effects:
##              Estimate Std. Error z value Pr(>|z|)   
## (Intercept)  0.167538   0.068662   2.440  0.01469 * 
## WvN          0.083729   0.112455   0.745  0.45654   
## WNvB        -0.324342   0.114429  -2.834  0.00459 **
## AlcQF       -0.006183   0.005951  -1.039  0.29881   
## WvN:AlcQF    0.006005   0.008147   0.737  0.46105   
## WNvB:AlcQF  -0.007977   0.009100  -0.876  0.38075   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) WvN    WNvB   AlcQF  WN:AQF
## WvN         0.023                            
## WNvB        0.039  0.002                     
## AlcQF      -0.025 -0.001  0.002              
## WvN:AlcQF  -0.001 -0.065 -0.001  0.032       
## WNvB:AlcQF  0.002 -0.001 -0.045  0.058  0.000

Models including condition

Mixed effects model

This mixed model tests a contrast between risks decisions made after Beer primes vs Water and Neutral primes combined (WNvB). It includes video group and beverage contrasts and their interaction.

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: response ~ WNvB + WvN + vidCvM * WNvB + vidBvW * WNvB + vidint *  
##     WNvB + (1 + BvW + BWvN | Subject) + (1 | pic2)
##    Data: gamble
## 
##      AIC      BIC   logLik deviance df.resid 
##  10181.2  10292.3  -5074.6  10149.2     7646 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3325 -0.9591  0.5911  0.8323  2.5800 
## 
## Random effects:
##  Groups  Name        Variance Std.Dev. Corr       
##  Subject (Intercept) 0.3006   0.5482              
##          BvW         0.6341   0.7963   -0.14      
##          BWvN        0.2930   0.5413   -0.07  0.32
##  pic2    (Intercept) 0.0109   0.1044              
## Number of obs: 7662, groups:  Subject, 112; pic2, 12
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)   
## (Intercept)  0.17015    0.06477   2.627  0.00862 **
## WNvB        -0.32999    0.10746  -3.071  0.00213 **
## WvN          0.05440    0.10855   0.501  0.61623   
## vidCvM       0.03190    0.05755   0.554  0.57940   
## vidBvW       0.03355    0.05730   0.585  0.55828   
## vidint       0.09612    0.05732   1.677  0.09359 . 
## WNvB:vidCvM -0.03825    0.08660  -0.442  0.65871   
## WNvB:vidBvW -0.08057    0.08631  -0.934  0.35056   
## WNvB:vidint -0.07271    0.08637  -0.842  0.39990   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) WNvB   WvN    vidCvM vidBvW vidint WNB:CM WNB:BW
## WNvB         0.069                                                 
## WvN          0.013  0.018                                          
## vidCvM       0.032  0.002 -0.002                                   
## vidBvW       0.013 -0.002  0.001 -0.018                            
## vidint      -0.015 -0.006 -0.001  0.014  0.036                     
## WNvB:vidCvM  0.002  0.029  0.001  0.098 -0.007 -0.002              
## WNvB:vidBvW -0.002  0.013  0.000 -0.007  0.097  0.003 -0.011       
## WNvB:vidint -0.007 -0.009 -0.001 -0.002  0.003  0.097  0.016  0.036

Standard analyses (ANOVA Ws)

These models test a Beer prime vs a Water prime contrast with both video factors included in the model. BvW is calculated as: proportion of high risk responses after beer primes - proportion of high risk responses after water primes. BvWrisk is calculated as: beer risk - water risk. Risk variable: high risk response/(hi + lo risk repsonses) for each prime type.

## 
## Call:
## lm(formula = BvW ~ vidCvM + vidBvW + vidint, data = gamble.w)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.97685 -0.29821 -0.01032  0.31724  1.18981 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  0.12707    0.04184   3.037  0.00299 **
## vidCvM       0.03342    0.04184   0.799  0.42613   
## vidBvW       0.03309    0.04184   0.791  0.43071   
## vidint       0.02555    0.04184   0.611  0.54266   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4424 on 108 degrees of freedom
## Multiple R-squared:  0.01468,    Adjusted R-squared:  -0.01269 
## F-statistic: 0.5365 on 3 and 108 DF,  p-value: 0.6583
## 
## Call:
## lm(formula = BvWrisk ~ vidCvM + vidBvW + vidint, data = gamble.w)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.52511 -0.14784 -0.00435  0.17115  0.67170 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  0.06760    0.02186   3.093  0.00252 **
## vidCvM       0.01722    0.02186   0.788  0.43250   
## vidBvW       0.01649    0.02186   0.754  0.45222   
## vidint       0.01394    0.02186   0.638  0.52492   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2311 on 108 degrees of freedom
## Multiple R-squared:  0.01431,    Adjusted R-squared:  -0.01307 
## F-statistic: 0.5226 on 3 and 108 DF,  p-value: 0.6677

This model tests a Beer prime vs a Neutral prime contrast with both video factors included in the model. BvN is calculated as: proportion of high risk responses after beer primes - proportion of high risk responses after neutral primes. BvNrisk is calculated as: beer risk - neutral risk. Risk variable: high risk response/(hi + lo risk repsonses) for each prime type.

## 
## Call:
## lm(formula = BvN ~ vidCvM + vidBvW + vidint, data = gamble.w)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.9361 -0.2353 -0.0246  0.2472  1.0756 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.151576   0.040465   3.746  0.00029 ***
## vidCvM      0.001973   0.040465   0.049  0.96120    
## vidBvW      0.033829   0.040465   0.836  0.40499    
## vidint      0.028671   0.040465   0.709  0.48014    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4278 on 108 degrees of freedom
## Multiple R-squared:  0.01066,    Adjusted R-squared:  -0.01682 
## F-statistic: 0.3878 on 3 and 108 DF,  p-value: 0.762
## 
## Call:
## lm(formula = BvNrisk ~ vidCvM + vidBvW + vidint, data = gamble.w)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.5067 -0.1361 -0.0120  0.1435  0.5886 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.080456   0.021454   3.750 0.000286 ***
## vidCvM      0.001588   0.021454   0.074 0.941147    
## vidBvW      0.017996   0.021454   0.839 0.403414    
## vidint      0.017003   0.021454   0.793 0.429787    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2268 on 108 degrees of freedom
## Multiple R-squared:  0.01182,    Adjusted R-squared:  -0.01563 
## F-statistic: 0.4307 on 3 and 108 DF,  p-value: 0.7314

This model tests a Water prime vs a Neutral prime contrast with both video factors included in the model. NvW is calculated as: proportion of high risk responses after neutral primes - proportion of high risk responses after water primes. NvWrisk is calculated as: neutral risk - water risk. Risk variable: high risk response/(hi + lo risk repsonses) for each prime type.

## 
## Call:
## lm(formula = NvW ~ vidCvM + vidBvW + vidint, data = gamble.w)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.84414 -0.25309 -0.03726  0.19946  1.33025 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0245040  0.0353450  -0.693    0.490
## vidCvM       0.0314484  0.0353450   0.890    0.376
## vidBvW      -0.0007385  0.0353450  -0.021    0.983
## vidint      -0.0031195  0.0353450  -0.088    0.930
## 
## Residual standard error: 0.3737 on 108 degrees of freedom
## Multiple R-squared:  0.007374,   Adjusted R-squared:  -0.0202 
## F-statistic: 0.2674 on 3 and 108 DF,  p-value: 0.8487
## 
## Call:
## lm(formula = NvWrisk ~ vidCvM + vidBvW + vidint, data = gamble.w)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.44213 -0.12616 -0.01753  0.10579  0.69875 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.012853   0.018363  -0.700    0.485
## vidCvM       0.015634   0.018363   0.851    0.396
## vidBvW      -0.001505   0.018363  -0.082    0.935
## vidint      -0.003060   0.018363  -0.167    0.868
## 
## Residual standard error: 0.1942 on 108 degrees of freedom
## Multiple R-squared:  0.007,  Adjusted R-squared:  -0.02058 
## F-statistic: 0.2538 on 3 and 108 DF,  p-value: 0.8585

Simples

BvW is calculated as: proportion of high risk responses after beer primes - proportion of high risk responses after water primes. BvWrisk is calculated as: beer risk - water risk. Risk variable: high risk response/(hi + lo risk repsonses) for each prime type.

BvN is calculated as: proportion of high risk responses after beer primes - proportion of high risk responses after neutral primes. BvNrisk is calculated as: beer risk - neutral risk. Risk variable: high risk response/(hi + lo risk repsonses) for each prime type.

CU Beer

## 
## Call:
## lm(formula = BvW ~ 1, data = gamble.w[gamble.w$Group == 1, ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.84414 -0.28164  0.03086  0.23920  1.03086 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  0.21914    0.07651   2.864  0.00817 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3976 on 26 degrees of freedom
## 
## Call:
## lm(formula = BvWrisk ~ 1, data = gamble.w[gamble.w$Group == 1, 
##     ])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.4359 -0.1497  0.0276  0.1293  0.5400 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  0.11526    0.03981   2.895  0.00758 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2069 on 26 degrees of freedom
## 
## Call:
## lm(formula = BvN ~ 1, data = gamble.w[gamble.w$Group == 1, ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.84105 -0.23688  0.03395  0.15895  0.78395 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  0.21605    0.07148   3.022  0.00557 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3714 on 26 degrees of freedom
## 
## Call:
## lm(formula = BvNrisk ~ 1, data = gamble.w[gamble.w$Group == 1, 
##     ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.44906 -0.12692  0.00417  0.07136  0.40470 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  0.11704    0.03735   3.134  0.00424 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1941 on 26 degrees of freedom

CU Water

## 
## Call:
## lm(formula = BvW ~ 1, data = gamble.w[gamble.w$Group == 2, ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.97685 -0.37269 -0.06019  0.35648  1.18981 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  0.10185    0.09743   1.045    0.305
## 
## Residual standard error: 0.5063 on 26 degrees of freedom
## 
## Call:
## lm(formula = BvWrisk ~ 1, data = gamble.w[gamble.w$Group == 2, 
##     ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.51882 -0.19504 -0.04249  0.17478  0.67170 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  0.05439    0.05123   1.062    0.298
## 
## Residual standard error: 0.2662 on 26 degrees of freedom
## 
## Call:
## lm(formula = BvN ~ 1, data = gamble.w[gamble.w$Group == 2, ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.88272 -0.23688  0.03395  0.26312  1.07562 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  0.09105    0.09229   0.987    0.333
## 
## Residual standard error: 0.4796 on 26 degrees of freedom
## 
## Call:
## lm(formula = BvNrisk ~ 1, data = gamble.w[gamble.w$Group == 2, 
##     ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.46602 -0.12330  0.01455  0.13897  0.58857 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  0.04704    0.04836   0.973     0.34
## 
## Residual standard error: 0.2513 on 26 degrees of freedom

MU Beer

## 
## Call:
## lm(formula = BvW ~ 1, data = gamble.w[gamble.w$Group == 3, ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.89286 -0.31994 -0.03869  0.31548  1.02381 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  0.10119    0.08699   1.163    0.255
## 
## Residual standard error: 0.4603 on 27 degrees of freedom
## 
## Call:
## lm(formula = BvWrisk ~ 1, data = gamble.w[gamble.w$Group == 3, 
##     ])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.4660 -0.1665 -0.0264  0.1577  0.5250 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  0.05293    0.04445   1.191    0.244
## 
## Residual standard error: 0.2352 on 27 degrees of freedom
## 
## Call:
## lm(formula = BvN ~ 1, data = gamble.w[gamble.w$Group == 3, ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.82143 -0.19643 -0.00893  0.28274  0.84524 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  0.15476    0.07685   2.014   0.0541 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4066 on 27 degrees of freedom
## 
## Call:
## lm(formula = BvNrisk ~ 1, data = gamble.w[gamble.w$Group == 3, 
##     ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.42769 -0.08777 -0.00140  0.13984  0.42014 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  0.07986    0.03929   2.033    0.052 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2079 on 27 degrees of freedom

MU Water

## 
## Call:
## lm(formula = BvW ~ 1, data = gamble.w[gamble.w$Group == 4, ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.91944 -0.24236 -0.00278  0.32014  0.62222 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  0.08611    0.07302   1.179    0.248
## 
## Residual standard error: 0.3999 on 29 degrees of freedom
## 
## Call:
## lm(formula = BvWrisk ~ 1, data = gamble.w[gamble.w$Group == 4, 
##     ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.52511 -0.12738 -0.00435  0.19165  0.31267 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  0.04783    0.03895   1.228    0.229
## 
## Residual standard error: 0.2134 on 29 degrees of freedom
## 
## Call:
## lm(formula = BvN ~ 1, data = gamble.w[gamble.w$Group == 4, ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.93611 -0.29028 -0.06111  0.23056  1.06389 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  0.14444    0.08122   1.779   0.0858 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4448 on 29 degrees of freedom
## 
## Call:
## lm(formula = BvNrisk ~ 1, data = gamble.w[gamble.w$Group == 4, 
##     ])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.5067 -0.1378 -0.0353  0.1630  0.5363 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  0.07788    0.04509   1.727   0.0948 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.247 on 29 degrees of freedom

Correlations

NIAAA Use Measures

These include raw and recoded (.re) values. P-values shown.

Correlation coefficients shown.

Combined

beer.dif = hi-risk beer responses - low-risk beer responses
water.dif = hi-risk water responses - low-risk water responses
neutral.dif = hi-risk neutral responses - low-risk neutral responses

P-values shown.

Correlation coefficients shown.

Study 2 (4b)

Descriptives and Chart

Prime Type Risk Response Proportion SD N
Beer High 0.574 0.180 66
Beer Low 0.361 0.170 66
Neutral High 0.465 0.165 66
Neutral Low 0.487 0.142 66
Water High 0.477 0.200 66
Water Low 0.473 0.180 66

Models

Simple Effects

Tests if the difference in propotions of high-risk responses is different from the proportions of low-risk for each prime category.

Beer

## 
## Call:
## lm(formula = beer.dif ~ 1, data = gamble.w)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -25.106  -4.106   1.394   5.894  16.894 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    5.106      1.008   5.066  3.6e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.188 on 65 degrees of freedom

Water

## 
## Call:
## lm(formula = water.dif ~ 1, data = gamble.w)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -20.0909  -5.8409   0.9091   7.9091  14.9091 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  0.09091    1.11102   0.082    0.935
## 
## Residual standard error: 9.026 on 65 degrees of freedom

Neutral

## 
## Call:
## lm(formula = neut.dif ~ 1, data = gamble.w)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -18.4697  -4.4697   0.5303   5.2803  14.5303 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  -0.5303     0.8919  -0.595    0.554
## 
## Residual standard error: 7.246 on 65 degrees of freedom

Mixed Effects Models

Beer vs Water

This model tests the effects of a Beer vs a Water prime on risk response.
Fixed effects:
       Beer/Water contrast: Beer = +1/2, Water = -1/2, Neutral = 0
       Orthogonal contrast: Beer = +1/3, Water = +1/3, Neutral = -2/3
Random effects:
       Subject: Intercept, slope for each contrast
       Stimuli(pic2): Intercept

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: response ~ BvW + BWvN + (1 + BvW + BWvN | Subject) + (1 | pic2)
##    Data: gamble
## 
##      AIC      BIC   logLik deviance df.resid 
##   5934.0   5998.1  -2957.0   5914.0     4482 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.4593 -0.9455  0.5729  0.8396  2.3742 
## 
## Random effects:
##  Groups  Name        Variance Std.Dev. Corr       
##  Subject (Intercept) 0.29755  0.5455              
##          BvW         1.02897  1.0144   -0.22      
##          BWvN        0.23781  0.4877    0.27  0.32
##  pic2    (Intercept) 0.02976  0.1725              
## Number of obs: 4492, groups:  Subject, 66; pic2, 12
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)   
## (Intercept)  0.13198    0.08963   1.472  0.14090   
## BvW          0.53168    0.19219   2.766  0.00567 **
## BWvN         0.30344    0.13873   2.187  0.02873 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##      (Intr) BvW   
## BvW  -0.108       
## BWvN  0.096  0.093

These models add questionnaire measures and interactions to the list of fixed effects and calculates a questionnaire measure slope for each stimuli picture.

Measures:
AlcQF - Alcohol use quantity-frequency product (drinks/month)
Risk - Risk taking after alcohol consumption
RAPI - Rutgers alcohol problem index
AE - Alcohol expectancy questionnaire (CEOA)

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: response ~ BvW + BWvN + AlcQF * BvW + AlcQF * BWvN + (1 + BvW +  
##     BWvN | Subject) + (1 + AlcQF | pic2)
##    Data: gamble
## 
##      AIC      BIC   logLik deviance df.resid 
##   5665.1   5760.6  -2817.6   5635.1     4272 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.4848 -0.9404  0.5688  0.8360  2.4141 
## 
## Random effects:
##  Groups  Name        Variance  Std.Dev. Corr       
##  Subject (Intercept) 3.180e-01 0.56394             
##          BvW         1.044e+00 1.02159  -0.24      
##          BWvN        2.477e-01 0.49772   0.25  0.29
##  pic2    (Intercept) 3.703e-02 0.19242             
##          AlcQF       1.452e-05 0.00381  -1.00      
## Number of obs: 4287, groups:  Subject, 63; pic2, 12
## 
## Fixed effects:
##              Estimate Std. Error z value Pr(>|z|)  
## (Intercept)  0.122016   0.096099   1.270   0.2042  
## BvW          0.499600   0.204643   2.441   0.0146 *
## BWvN         0.282200   0.150150   1.880   0.0602 .
## AlcQF       -0.002118   0.009785  -0.216   0.8287  
## BvW:AlcQF    0.017343   0.019142   0.906   0.3649  
## BWvN:AlcQF  -0.003782   0.011753  -0.322   0.7476  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) BvW    BWvN   AlcQF  BW:AQF
## BvW        -0.111                            
## BWvN        0.086  0.078                     
## AlcQF      -0.066  0.002  0.002              
## BvW:AlcQF   0.002 -0.093  0.001 -0.176       
## BWvN:AlcQF  0.003  0.002 -0.160  0.167  0.168
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: response ~ BvW + BWvN + Risk * BvW + Risk * BWvN + (1 + BvW +  
##     BWvN | Subject) + (1 + Risk | pic2)
##    Data: gamble
## 
##      AIC      BIC   logLik deviance df.resid 
##   5934.8   6031.0  -2952.4   5904.8     4477 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.7414 -0.9357  0.5778  0.8355  2.3929 
## 
## Random effects:
##  Groups  Name        Variance Std.Dev. Corr       
##  Subject (Intercept) 0.277207 0.5265              
##          BvW         1.039112 1.0194   -0.25      
##          BWvN        0.243954 0.4939    0.29  0.33
##  pic2    (Intercept) 0.029690 0.1723              
##          Risk        0.008081 0.0899   -0.74      
## Number of obs: 4492, groups:  Subject, 66; pic2, 12
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)   
## (Intercept)  0.13253    0.08788   1.508  0.13153   
## BvW          0.53544    0.19258   2.780  0.00543 **
## BWvN         0.30416    0.13902   2.188  0.02868 * 
## Risk         0.14559    0.07702   1.890  0.05872 . 
## BvW:Risk     0.09767    0.16271   0.600  0.54831   
## BWvN:Risk   -0.01341    0.10625  -0.126  0.89957   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##           (Intr) BvW    BWvN   Risk   BvW:Rs
## BvW       -0.118                            
## BWvN       0.104  0.096                     
## Risk      -0.139  0.004  0.004              
## BvW:Risk   0.004 -0.177  0.004 -0.150       
## BWvN:Risk  0.004  0.004 -0.290  0.159  0.157
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: response ~ BvW + BWvN + RAPI * BvW + RAPI * BWvN + (1 + BvW +  
##     BWvN | Subject) + (1 + RAPI | pic2)
##    Data: gamble
## 
##      AIC      BIC   logLik deviance df.resid 
##   5939.9   6036.0  -2954.9   5909.9     4477 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.5182 -0.9471  0.5709  0.8410  2.3844 
## 
## Random effects:
##  Groups  Name        Variance  Std.Dev. Corr       
##  Subject (Intercept) 2.789e-01 0.528156            
##          BvW         1.031e+00 1.015490 -0.23      
##          BWvN        2.385e-01 0.488406  0.29  0.32
##  pic2    (Intercept) 2.975e-02 0.172478            
##          RAPI        1.315e-06 0.001147 -1.00      
## Number of obs: 4492, groups:  Subject, 66; pic2, 12
## 
## Fixed effects:
##               Estimate Std. Error z value Pr(>|z|)   
## (Intercept)  0.1321050  0.0880409   1.500  0.13349   
## BvW          0.5321226  0.1922882   2.767  0.00565 **
## BWvN         0.3035594  0.1387694   2.188  0.02871 * 
## RAPI         0.0188077  0.0100236   1.876  0.06061 . 
## BvW:RAPI     0.0001852  0.0204797   0.009  0.99279   
## BWvN:RAPI   -0.0034730  0.0124243  -0.280  0.77984   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##           (Intr) BvW    BWvN   RAPI   BW:RAP
## BvW       -0.109                            
## BWvN       0.103  0.093                     
## RAPI      -0.022  0.004  0.001              
## BvW:RAPI   0.004 -0.028  0.003 -0.167       
## BWvN:RAPI  0.001  0.004 -0.049  0.189  0.192
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: response ~ BvW + BWvN + AE * BvW + AE * BWvN + (1 + BvW + BWvN |  
##     Subject) + (1 + AE | pic2)
##    Data: gamble
## 
##      AIC      BIC   logLik deviance df.resid 
##   5941.1   6037.2  -2955.5   5911.1     4477 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.4611 -0.9414  0.5719  0.8440  2.3828 
## 
## Random effects:
##  Groups  Name        Variance  Std.Dev. Corr       
##  Subject (Intercept) 2.945e-01 0.542646            
##          BvW         1.011e+00 1.005368 -0.24      
##          BWvN        2.270e-01 0.476497  0.25  0.30
##  pic2    (Intercept) 2.974e-02 0.172449            
##          AE          6.344e-06 0.002519 -1.00      
## Number of obs: 4492, groups:  Subject, 66; pic2, 12
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)   
## (Intercept) 0.131670   0.089362   1.473  0.14063   
## BvW         0.531137   0.191458   2.774  0.00553 **
## BWvN        0.303186   0.138123   2.195  0.02816 * 
## AE          0.006774   0.008962   0.756  0.44975   
## BvW:AE      0.018969   0.017835   1.064  0.28751   
## BWvN:AE     0.012118   0.010815   1.121  0.26251   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##         (Intr) BvW    BWvN   AE     BvW:AE
## BvW     -0.115                            
## BWvN     0.089  0.085                     
## AE      -0.042 -0.001  0.001              
## BvW:AE  -0.001 -0.059 -0.001 -0.170       
## BWvN:AE  0.001 -0.001 -0.105  0.163  0.180

This model includes all the questionnaire measures described above as fixed effects. Only intercepts were calculated for random factors due to convergence limitations.

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: 
## response ~ BvW + BWvN + AlcQF + Risk + RAPI + AE + (1 | Subject) +  
##     (1 | pic2)
##    Data: gamble
## 
##      AIC      BIC   logLik deviance df.resid 
##   5734.1   5791.4  -2858.0   5716.1     4278 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.0243 -0.9639  0.6045  0.8820  1.6984 
## 
## Random effects:
##  Groups  Name        Variance Std.Dev.
##  Subject (Intercept) 0.24057  0.4905  
##  pic2    (Intercept) 0.02882  0.1698  
## Number of obs: 4287, groups:  Subject, 63; pic2, 12
## 
## Fixed effects:
##              Estimate Std. Error z value Pr(>|z|)   
## (Intercept)  0.113543   0.085183   1.333  0.18256   
## BvW          0.446701   0.143346   3.116  0.00183 **
## BWvN         0.277281   0.123736   2.241  0.02503 * 
## AlcQF       -0.015656   0.009796  -1.598  0.11001   
## Risk         0.141890   0.086950   1.632  0.10271   
## RAPI         0.018680   0.011280   1.656  0.09772 . 
## AE          -0.003394   0.009687  -0.350  0.72610   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##       (Intr) BvW    BWvN   AlcQF  Risk   RAPI  
## BvW    0.004                                   
## BWvN   0.003  0.005                            
## AlcQF  0.014 -0.003 -0.001                     
## Risk  -0.018  0.003  0.002 -0.187              
## RAPI  -0.019  0.004  0.001 -0.340 -0.287       
## AE    -0.005 -0.002 -0.001  0.036 -0.432 -0.058

Beer vs Neutral

This model tests the effects of a Beer vs a Neutral prime on risk response.
Fixed effects:
       Beer/Neutral contrast: Beer = +1/2, Neutral = -1/2, Water = 0
       Orthogonal contrast: Beer = +1/3, Neutral = +1/3, Water = -2/3
Random effects:
       Subject: Intercept, slope for each contrast
       Stimuli(pic2): Intercept

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: response ~ BvN + BNvW + (1 + BvW + BWvN | Subject) + (1 | pic2)
##    Data: gamble
## 
##      AIC      BIC   logLik deviance df.resid 
##   5934.0   5998.1  -2957.0   5914.0     4482 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.4593 -0.9455  0.5729  0.8396  2.3742 
## 
## Random effects:
##  Groups  Name        Variance Std.Dev. Corr       
##  Subject (Intercept) 0.29755  0.5455              
##          BvW         1.02896  1.0144   -0.22      
##          BWvN        0.23782  0.4877    0.27  0.32
##  pic2    (Intercept) 0.02976  0.1725              
## Number of obs: 4492, groups:  Subject, 66; pic2, 12
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)   
## (Intercept)  0.13198    0.08963   1.472  0.14090   
## BvN          0.56930    0.17594   3.236  0.00121 **
## BNvW         0.24704    0.15406   1.604  0.10882   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##      (Intr) BvN   
## BvN   0.017       
## BNvW -0.144  0.202

These models add questionnaire measures and interactions to the list of fixed effects and calculates a questionnaire measure slope for each stimuli picture.

Measures:
AlcQF - Alcohol use quantity-frequency product (drinks/month)
Risk - Risk taking after alcohol consumption
RAPI - Rutgers alcohol problem index
AE - Alcohol expectancy questionnaire (CEOA)

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: response ~ BvN + BNvW + AlcQF.c * BvN + AlcQF.c * BNvW + (1 +  
##     BvN + BNvW | Subject) + (1 + AlcQF.c | pic2)
##    Data: gamble
## 
##      AIC      BIC   logLik deviance df.resid 
##   5665.1   5760.6  -2817.6   5635.1     4272 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.4848 -0.9404  0.5688  0.8360  2.4141 
## 
## Random effects:
##  Groups  Name        Variance  Std.Dev. Corr       
##  Subject (Intercept) 3.180e-01 0.56394             
##          BvN         6.574e-01 0.81083   0.00      
##          BNvW        5.374e-01 0.73305  -0.34  0.58
##  pic2    (Intercept) 3.702e-02 0.19242             
##          AlcQF.c     1.452e-05 0.00381  -1.00      
## Number of obs: 4287, groups:  Subject, 63; pic2, 12
## 
## Fixed effects:
##               Estimate Std. Error z value Pr(>|z|)   
## (Intercept)   0.122005   0.096100   1.270   0.2042   
## BvN           0.531998   0.188200   2.827   0.0047 **
## BNvW          0.233607   0.165518   1.411   0.1581   
## AlcQF.c      -0.002117   0.009785  -0.216   0.8287   
## BvN:AlcQF.c   0.004888   0.016353   0.299   0.7650   
## BNvW:AlcQF.c  0.014897   0.014573   1.022   0.3066   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) BvN    BNvW   AlcQF. BN:AQF
## BvN          0.008                            
## BNvW        -0.142  0.181                     
## AlcQF.c     -0.066  0.003  0.001              
## BvN:AlcQF.c  0.003 -0.120  0.002  0.016       
## BNvW:AlcQF.  0.001  0.002 -0.116 -0.241  0.366
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: response ~ BvN + BNvW + Risk * BvN + Risk * BNvW + (1 + BvN +  
##     BNvW | Subject) + (1 + Risk | pic2)
##    Data: gamble
## 
##      AIC      BIC   logLik deviance df.resid 
##   5934.8   6031.0  -2952.4   5904.8     4477 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.7414 -0.9357  0.5778  0.8355  2.3930 
## 
## Random effects:
##  Groups  Name        Variance Std.Dev. Corr       
##  Subject (Intercept) 0.277214 0.52651             
##          BvN         0.668628 0.81770   0.02      
##          BNvW        0.521802 0.72236  -0.36  0.59
##  pic2    (Intercept) 0.029692 0.17231             
##          Risk        0.008081 0.08989  -0.74      
## Number of obs: 4492, groups:  Subject, 66; pic2, 12
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)   
## (Intercept)  0.13253    0.08788   1.508   0.1315   
## BvN          0.57187    0.17655   3.239   0.0012 **
## BNvW         0.24949    0.15416   1.618   0.1056   
## Risk         0.14559    0.07702   1.890   0.0587 . 
## BvN:Risk     0.03543    0.14363   0.247   0.8052   
## BNvW:Risk    0.07996    0.12520   0.639   0.5230   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##           (Intr) BvN    BNvW   Risk   BvN:Rs
## BvN        0.017                            
## BNvW      -0.158  0.203                     
## Risk      -0.139  0.005  0.002              
## BvN:Risk   0.006 -0.220  0.005  0.033       
## BNvW:Risk  0.003  0.005 -0.221 -0.214  0.314
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: response ~ BvN + BNvW + RAPI * BvN + RAPI * BNvW + (1 + BvN +  
##     BNvW | Subject) + (1 + RAPI | pic2)
##    Data: gamble
## 
##      AIC      BIC   logLik deviance df.resid 
##   5939.9   6036.0  -2954.9   5909.9     4477 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.5182 -0.9471  0.5709  0.8410  2.3844 
## 
## Random effects:
##  Groups  Name        Variance  Std.Dev. Corr       
##  Subject (Intercept) 2.790e-01 0.528167            
##          BvN         6.561e-01 0.810026  0.03      
##          BNvW        5.199e-01 0.721025 -0.34  0.59
##  pic2    (Intercept) 2.975e-02 0.172492            
##          RAPI        1.316e-06 0.001147 -1.00      
## Number of obs: 4492, groups:  Subject, 66; pic2, 12
## 
## Fixed effects:
##              Estimate Std. Error z value Pr(>|z|)   
## (Intercept)  0.132111   0.088044   1.501  0.13348   
## BvN          0.569612   0.176030   3.236  0.00121 **
## BNvW         0.247332   0.154091   1.605  0.10847   
## RAPI         0.018812   0.010024   1.877  0.06055 . 
## BvN:RAPI    -0.003381   0.017549  -0.193  0.84722   
## BNvW:RAPI    0.001878   0.015426   0.122  0.90310   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##           (Intr) BvN    BNvW   RAPI   BN:RAP
## BvN        0.022                            
## BNvW      -0.148  0.202                     
## RAPI      -0.022  0.003  0.003              
## BvN:RAPI   0.003 -0.033  0.002  0.036       
## BNvW:RAPI  0.004  0.002 -0.038 -0.243  0.386
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: response ~ BvN + BNvW + AE * BvN + AE * BNvW + (1 + BvN + BNvW |  
##     Subject) + (1 + AE | pic2)
##    Data: gamble
## 
##      AIC      BIC   logLik deviance df.resid 
##   5941.1   6037.2  -2955.5   5911.1     4477 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.4612 -0.9414  0.5719  0.8440  2.3828 
## 
## Random effects:
##  Groups  Name        Variance  Std.Dev. Corr       
##  Subject (Intercept) 2.945e-01 0.542647            
##          BvN         6.243e-01 0.790129  0.00      
##          BNvW        5.169e-01 0.718949 -0.34  0.59
##  pic2    (Intercept) 2.974e-02 0.172444            
##          AE          6.342e-06 0.002518 -1.00      
## Number of obs: 4492, groups:  Subject, 66; pic2, 12
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)   
## (Intercept) 0.131683   0.089361   1.474  0.14059   
## BvN         0.568792   0.174632   3.257  0.00113 **
## BNvW        0.246758   0.153928   1.603  0.10892   
## AE          0.006773   0.008962   0.756  0.44979   
## BvN:AE      0.021602   0.015204   1.421  0.15538   
## BNvW:AE     0.008169   0.013496   0.605  0.54496   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##         (Intr) BvN    BNvW   AE     BvN:AE
## BvN      0.007                            
## BNvW    -0.147  0.198                     
## AE      -0.042  0.000 -0.002              
## BvN:AE   0.000 -0.079  0.001  0.016       
## BNvW:AE -0.002  0.001 -0.073 -0.234  0.381

This model includes all the questionnaire measures described above as fixed effects. Only intercepts were calculated for random factors due to convergence limitations.

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: 
## response ~ BvN + BNvW + AlcQF + Risk + RAPI + AE + (1 | Subject) +  
##     (1 | pic2)
##    Data: gamble
## 
##      AIC      BIC   logLik deviance df.resid 
##   5734.1   5791.4  -2858.0   5716.1     4278 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.0243 -0.9639  0.6044  0.8820  1.6984 
## 
## Random effects:
##  Groups  Name        Variance Std.Dev.
##  Subject (Intercept) 0.24057  0.4905  
##  pic2    (Intercept) 0.02882  0.1698  
## Number of obs: 4287, groups:  Subject, 63; pic2, 12
## 
## Fixed effects:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  0.113536   0.085183   1.333 0.182581    
## BvN          0.500636   0.143300   3.494 0.000476 ***
## BNvW         0.196392   0.123775   1.587 0.112584    
## AlcQF       -0.015656   0.009796  -1.598 0.110010    
## Risk         0.141897   0.086951   1.632 0.102696    
## RAPI         0.018680   0.011280   1.656 0.097721 .  
## AE          -0.003393   0.009687  -0.350 0.726130    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##       (Intr) BvN    BNvW   AlcQF  Risk   RAPI  
## BvN    0.004                                   
## BNvW   0.002  0.005                            
## AlcQF  0.014 -0.003 -0.003                     
## Risk  -0.018  0.004  0.002 -0.187              
## RAPI  -0.019  0.003  0.002 -0.340 -0.287       
## AE    -0.005 -0.001 -0.001  0.036 -0.432 -0.058

Water vs Neutral

This model tests the effects of a Water vs a Neutral prime on risk response.
Fixed effects:
       Water/Neutral contrast: Water = +1/2, Neutral = -1/2, Beer = 0
       Orthogonal contrast: Water = +1/3, Neutral = +1/3, Beer = -2/3
Random effects:
       Subject: Intercept, slope for each contrast
       Stimuli(pic2): Intercept

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: response ~ WvN + WNvB + (1 + BvW + BWvN | Subject) + (1 | pic2)
##    Data: gamble
## 
##      AIC      BIC   logLik deviance df.resid 
##   5934.0   5998.1  -2957.0   5914.0     4482 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.4593 -0.9455  0.5729  0.8396  2.3742 
## 
## Random effects:
##  Groups  Name        Variance Std.Dev. Corr       
##  Subject (Intercept) 0.29754  0.5455              
##          BvW         1.02899  1.0144   -0.22      
##          BWvN        0.23781  0.4877    0.27  0.32
##  pic2    (Intercept) 0.02976  0.1725              
## Number of obs: 4492, groups:  Subject, 66; pic2, 12
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  0.13198    0.08963   1.472 0.140900    
## WvN          0.03759    0.16128   0.233 0.815692    
## WNvB        -0.55051    0.16567  -3.323 0.000891 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##      (Intr) WvN  
## WvN  0.147       
## WNvB 0.053  0.112

These models add questionnaire measures and interactions to the list of fixed effects and calculates a questionnaire measure slope for each stimuli picture.

Measures:
AlcQF - Alcohol use quantity-frequency product (drinks/month)
Risk - Risk taking after alcohol consumption
RAPI - Rutgers alcohol problem index
AE - Alcohol expectancy questionnaire (CEOA)

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: response ~ WvN + WNvB + AlcQF * WvN + AlcQF * WNvB + (1 + WvN +  
##     WNvB | Subject) + (1 + AlcQF | pic2)
##    Data: gamble
## 
##      AIC      BIC   logLik deviance df.resid 
##   5665.1   5760.6  -2817.6   5635.1     4272 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.4848 -0.9404  0.5688  0.8360  2.4141 
## 
## Random effects:
##  Groups  Name        Variance  Std.Dev. Corr      
##  Subject (Intercept) 3.180e-01 0.56394            
##          WvN         3.598e-01 0.59986  0.41      
##          WNvB        7.606e-01 0.87212  0.14  0.37
##  pic2    (Intercept) 3.702e-02 0.19242            
##          AlcQF       1.452e-05 0.00381  -1.00     
## Number of obs: 4287, groups:  Subject, 63; pic2, 12
## 
## Fixed effects:
##              Estimate Std. Error z value Pr(>|z|)   
## (Intercept)  0.122011   0.096100   1.270  0.20421   
## WvN          0.032401   0.174975   0.185  0.85309   
## WNvB        -0.515811   0.176049  -2.930  0.00339 **
## AlcQF       -0.002118   0.009785  -0.216  0.82867   
## WvN:AlcQF   -0.012454   0.013858  -0.899  0.36880   
## WNvB:AlcQF  -0.011115   0.016398  -0.678  0.49787   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) WvN    WNvB   AlcQF  WN:AQF
## WvN         0.138                            
## WNvB        0.060  0.105                     
## AlcQF      -0.066  0.001 -0.002              
## WvN:AlcQF   0.001 -0.156  0.001  0.263       
## WNvB:AlcQF -0.003  0.001 -0.094  0.095  0.218
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: response ~ WvN + WNvB + Risk * WvN + Risk * WNvB + (1 + WvN +  
##     WNvB | Subject) + (1 + Risk | pic2)
##    Data: gamble
## 
##      AIC      BIC   logLik deviance df.resid 
##   5934.8   6031.0  -2952.4   5904.8     4477 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.7414 -0.9357  0.5778  0.8355  2.3929 
## 
## Random effects:
##  Groups  Name        Variance Std.Dev. Corr      
##  Subject (Intercept) 0.277219 0.52652            
##          WvN         0.338815 0.58208  0.47      
##          WNvB        0.769179 0.87703  0.14  0.36
##  pic2    (Intercept) 0.029691 0.17231            
##          Risk        0.008081 0.08989  -0.74     
## Number of obs: 4492, groups:  Subject, 66; pic2, 12
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  0.13254    0.08788   1.508 0.131516    
## WvN          0.03644    0.16134   0.226 0.821300    
## WNvB        -0.55366    0.16618  -3.332 0.000863 ***
## Risk         0.14559    0.07702   1.890 0.058725 .  
## WvN:Risk    -0.06225    0.12323  -0.505 0.613483    
## WNvB:Risk   -0.06655    0.14055  -0.473 0.635875    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##           (Intr) WvN    WNvB   Risk   WvN:Rs
## WvN        0.160                            
## WNvB       0.060  0.110                     
## Risk      -0.139  0.001 -0.005              
## WvN:Risk   0.001 -0.290  0.001  0.236       
## WNvB:Risk -0.006  0.001 -0.177  0.070  0.169
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: response ~ WvN + WNvB + RAPI * WvN + RAPI * WNvB + (1 + WvN +  
##     WNvB | Subject) + (1 + RAPI | pic2)
##    Data: gamble
## 
##      AIC      BIC   logLik deviance df.resid 
##   5939.9   6036.0  -2954.9   5909.9     4477 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.5182 -0.9471  0.5709  0.8410  2.3843 
## 
## Random effects:
##  Groups  Name        Variance  Std.Dev. Corr      
##  Subject (Intercept) 2.790e-01 0.528167           
##          WvN         3.365e-01 0.580079 0.45      
##          WNvB        7.595e-01 0.871486 0.12  0.37
##  pic2    (Intercept) 2.975e-02 0.172486           
##          RAPI        1.313e-06 0.001146 -1.00     
## Number of obs: 4492, groups:  Subject, 66; pic2, 12
## 
## Fixed effects:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  0.132120   0.088042   1.501 0.133447    
## WvN          0.037514   0.161280   0.233 0.816070    
## WNvB        -0.550865   0.165750  -3.323 0.000889 ***
## RAPI         0.018810   0.010024   1.877 0.060579 .  
## WvN:RAPI    -0.003569   0.014507  -0.246 0.805665    
## WNvB:RAPI    0.001597   0.017637   0.091 0.927842    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##           (Intr) WvN    WNvB   RAPI   WN:RAP
## WvN        0.154                            
## WNvB       0.051  0.112                     
## RAPI      -0.022 -0.002 -0.004              
## WvN:RAPI  -0.002 -0.052 -0.001  0.280       
## WNvB:RAPI -0.004 -0.001 -0.026  0.079  0.218
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: response ~ WvN + WNvB + AE * WvN + AE * WNvB + (1 + WvN + WNvB |  
##     Subject) + (1 + AE | pic2)
##    Data: gamble
## 
##      AIC      BIC   logLik deviance df.resid 
##   5941.1   6037.2  -2955.5   5911.1     4477 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.4611 -0.9414  0.5719  0.8440  2.3828 
## 
## Random effects:
##  Groups  Name        Variance  Std.Dev. Corr      
##  Subject (Intercept) 2.945e-01 0.542642           
##          WvN         3.352e-01 0.578936 0.42      
##          WNvB        7.338e-01 0.856598 0.14  0.39
##  pic2    (Intercept) 2.974e-02 0.172444           
##          AE          6.343e-06 0.002519 -1.00     
## Number of obs: 4492, groups:  Subject, 66; pic2, 12
## 
## Fixed effects:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  0.131689   0.089361   1.474 0.140569    
## WvN          0.037660   0.161201   0.234 0.815278    
## WNvB        -0.549958   0.164551  -3.342 0.000831 ***
## AE           0.006773   0.008962   0.756 0.449791    
## WvN:AE       0.002631   0.012720   0.207 0.836108    
## WNvB:AE     -0.020286   0.015303  -1.326 0.184964    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##         (Intr) WvN    WNvB   AE     WvN:AE
## WvN      0.145                            
## WNvB     0.063  0.116                     
## AE      -0.042  0.002  0.001              
## WvN:AE   0.002 -0.100  0.002  0.258       
## WNvB:AE  0.001  0.002 -0.061  0.091  0.223

This model includes all the questionnaire measures described above as fixed effects. Only intercepts were calculated for random factors due to convergence limitations.

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: 
## response ~ WvN + WNvB + AlcQF + Risk + RAPI + AE + (1 | Subject) +  
##     (1 | pic2)
##    Data: gamble
## 
##      AIC      BIC   logLik deviance df.resid 
##   5734.1   5791.4  -2858.0   5716.1     4278 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.0243 -0.9639  0.6045  0.8820  1.6984 
## 
## Random effects:
##  Groups  Name        Variance Std.Dev.
##  Subject (Intercept) 0.24057  0.4905  
##  pic2    (Intercept) 0.02882  0.1698  
## Number of obs: 4287, groups:  Subject, 63; pic2, 12
## 
## Fixed effects:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  0.113547   0.085181   1.333 0.182529    
## WvN          0.053927   0.142689   0.378 0.705477    
## WNvB        -0.473671   0.124300  -3.811 0.000139 ***
## AlcQF       -0.015655   0.009796  -1.598 0.110040    
## Risk         0.141896   0.086951   1.632 0.102700    
## RAPI         0.018680   0.011280   1.656 0.097720 .  
## AE          -0.003394   0.009687  -0.350 0.726043    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##       (Intr) WvN    WNvB   AlcQF  Risk   RAPI  
## WvN    0.000                                   
## WNvB  -0.005  0.000                            
## AlcQF  0.014  0.001  0.003                     
## Risk  -0.018  0.000 -0.004 -0.187              
## RAPI  -0.019 -0.001 -0.004 -0.340 -0.287       
## AE    -0.005  0.000  0.002  0.036 -0.432 -0.058

Correlations

NIAAA Use Measures

These include raw and recoded (.re) values. P-values shown.

Correlation coefficients shown.

Combined

beer.dif = hi-risk beer responses - low-risk beer responses
water.dif = hi-risk water responses - low-risk water responses
neutral.dif = hi-risk neutral responses - low-risk neutral responses
AlcQF - Alcohol use quantity-frequency product (drinks/month)
Risk - Risk taking after alcohol consumption (alpha = .86)
RAPI - Rutgers alcohol problem index (alpha = .83)
AE - Alcohol expectancy questionnaire (CEOA, alpha = .74)

P-values shown.

Correlation coefficients shown.